RWA Logistic Functions

RWA Logistic Functions - Logistic Functions Real World...

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Logistic Functions Real World Applications Population Modeling Here is some data from the US Census Bureau on populations of four states from 1900 to 2000. The values given are in millions: Year Florida Pennsylvania Arizona New York 1900 0.5 6.3 0.1 7.3 1910 0.8 7.7 0.2 9.1 1920 1.0 8.7 0.3 10.3 1930 1.5 9.6 0.4 12.6 1940 1.9 9.9 0.5 13.5 1950 2.8 10.5 0.7 14.8 1960 5.0 11.3 1.3 16.8 1970 6.8 11.8 1.8 18.2 1980 9.7 11.9 2.7 17.6 1990 12.9 11.9 3.7 18.0 2000 16.0 12.3 5.1 19.0 We can create a logistic model for population using this data. By doing so, we can get an estimate of the maximum sustainable population a state can have. To start, let’s work with Florida. Because the values for ‘year’ are so large, we’re better off not using the values given, but smaller comparative values. In other words, let’s use 100 to represent 1900, 110 to represent 1910 and so forth. (Think of the values as the number of years after 1800). Put that data into L1. Put the population values for Florida into L2. Create a scatterplot of those values. Don’t forget to use ZOOM-9 to get the best picture. You should see this: You can see that the curve has an exponential look initially, and has a hint of leveling off at the top.
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We can have our calculator create a logistic regression model. To do that, follow these
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RWA Logistic Functions - Logistic Functions Real World...

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